def test_simulation_time(num_simulations=100):
start_time = time()
for i in trange(num_simulations):
event = SimulatedFRB()
event.simulateFRB()
end_time = time()
print(
f"{num_simulations} sims completed in {end_time - start_time} seconds")
def real_RFI_plot(RFI_array_file,
seed=24,
figsize=(12, 8),
SNRmin=5,
SNRmax=15):
np.random.seed(seed)
npz_file = np.load(RFI_array_file)
real_RFI = npz_file['rfi_data']
frequencies = npz_file['freq']
weights = npz_file['weights']
print(f"Frequencies: {frequencies}")
random_indexes = np.random.randint(low=0, high=len(real_RFI), size=8)
sample_RFI, sample_weights = real_RFI[random_indexes], weights[
random_indexes]
fig_RFI, ax_RFI = plt.subplots(nrows=4, ncols=2, figsize=figsize)
for RFI_image, w, ax in zip(sample_RFI, sample_weights, ax_RFI.flatten()):
event = SimulatedFRB(f_low=1850,
f_high=2700,
f_ref=np.median(frequencies),
bandwidth=np.ptp(frequencies))
event.simulateFRB(background=RFI_image,
weights=w,
SNRmin=SNRmin,
SNRmax=SNRmax)
ax.imshow(event.simulatedFRB,
aspect='auto',
extent=[0, 256,
np.min(frequencies),
np.max(frequencies)])
ax.set_title(f'SNR: {np.round(event.SNR, 1)}')
fig_RFI.tight_layout()
return fig_RFI
def noise_and_FRB(nrows=4, ncols=2):
fig, ax = plt.subplots(nrows=nrows, ncols=ncols)
example_number = 1
flat_axes = ax.flatten()
while example_number < len(flat_axes):
event = SimulatedFRB()
event.simulateFRB()
frb = event.simulatedFRB
# collapse all frequencies by taking mean for each column
profile_1D = np.mean(frb, axis=0)
# plot results for every 3 axes
flat_axes[example_number - 1].imshow(frb)
flat_axes[example_number - 1].set_title(
f"Noise and FRB (SNR: {np.round(event.SNR, 2)})")
flat_axes[example_number].plot(profile_1D)
flat_axes[example_number].set_title(f"Profile")
example_number += 2
fig.tight_layout()
return fig